Big data, data mining, and machine learning value creation for business leaders and practitioners
With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitio...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
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Wiley
2014.
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| Edición: | 1st edition |
| Colección: | Wiley and SAS Business Series
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629987206719 |
Tabla de Contenidos:
- ""Epigraph""; ""Series""; ""Titlepage""; ""Copyright""; ""Dedication""; ""Foreword""; ""Preface""; ""Notes""; ""Acknowledgments""; ""Introduction""; ""Big Data Timeline""; ""Why This Topic Is Relevant Now""; ""Is Big Data a Fad?""; ""Where Using Big Data Makes a Big Difference""; ""Notes""; ""Part One The Computing Environment""; ""Chapter 1 Hardware""; ""Storage (Disk)""; ""Central Processing Unit""; ""Memory""; ""Network""; ""Notes""; ""Chapter 2 Distributed Systems""; ""Database Computing""; ""File System Computing""; ""Considerations""; ""Notes""; ""Chapter 3 Analytical Tools""; ""Weka""
- ""Java and JVM Languages""""R""; ""Python""; ""SAS""; ""Notes""; ""Part Two Turning Data into Business Value""; ""Chapter 4 Predictive Modeling""; ""A Methodology for Building Models""; ""sEMMA""; ""Binary Classification""; ""Multilevel Classification""; ""Interval Prediction""; ""Assessment of Predictive Models""; ""Notes""; ""Chapter 5 Common Predictive Modeling Techniques""; ""RFM""; ""Regression""; ""Generalized Linear Models""; ""Neural Networks""; ""Decision and Regression Trees""; ""Support Vector Machines""; ""Bayesian Methods Network Classification""; ""Ensemble Methods""; ""Notes""
- ""Chapter 6 Segmentation""""Cluster Analysis""; ""Distance Measures (Metrics)""; ""Evaluating Clustering""; ""Number of Clusters""; ""K-means Algorithm""; ""Hierarchical Clustering""; ""Profiling Clusters""; ""Notes""; ""Chapter 7 Incremental Response Modeling""; ""Building the Response Model""; ""Measuring the Incremental Response""; ""Chapter 8 Time Series Data Mining""; ""Reducing Dimensionality""; ""Detecting Patterns""; ""Time Series Data Mining in Action: Nike+ FuelBand""; ""Notes""; ""Chapter 9 Recommendation Systems""; ""What Are Recommendation Systems?""; ""Where Are They Used?""
- ""How Do They Work?""""Assessing Recommendation Quality""; ""Recommendations in Action: SAS Library""; ""Notes""; ""Chapter 10 Text Analytics""; ""Information Retrieval""; ""Content Categorization""; ""Text Mining""; ""Text Analytics in Action: Let's Play Jeopardy!""; ""Notes""; ""Part Three Success Stories of Putting It All Together""; ""Qualities of Successful Projects""; ""Chapter 11 Case Study of a Large U.S.-Based Financial Services Company""; ""Traditional Marketing Campaign Process""; ""High-Performance Marketing Solution""; ""Value Proposition for Change""
- ""Chapter 12 Case Study of a Major Health Care Provider""""CAHPS""; ""HEDIS""; ""HOS""; ""IRE""; ""Chapter 13 Case Study of a Technology Manufacturer""; ""Finding Defective Devices""; ""How They Reduced Cost""; ""Chapter 14 Case Study of Online Brand Management""; ""Chapter 15 Case Study of Mobile Application Recommendations""; ""Chapter 16 Case Study of a High-Tech Product Manufacturer""; ""Handling the Missing Data""; ""Application beyond Manufacturing""; ""Chapter 17 Looking to the Future""; ""Reproducible Research""; ""Privacy with Public Data Sets""; ""The Internet of Things""
- ""Software Development in the Future""
